"""simple docstring"""



import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    []
       for i in range(encoder_config.num_hidden_layers				):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.norm1.weight", F"encoder.encoder.layer.{i}.layernorm_before.weight")				)
              rename_keys.append((F"encoder.deit.blocks.{i}.norm1.bias", F"encoder.encoder.layer.{i}.layernorm_before.bias")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.attn.proj.weight", F"encoder.encoder.layer.{i}.attention.output.dense.weight")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.attn.proj.bias", F"encoder.encoder.layer.{i}.attention.output.dense.bias")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.norm2.weight", F"encoder.encoder.layer.{i}.layernorm_after.weight")				)
              rename_keys.append((F"encoder.deit.blocks.{i}.norm2.bias", F"encoder.encoder.layer.{i}.layernorm_after.bias")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.mlp.fc1.weight", F"encoder.encoder.layer.{i}.intermediate.dense.weight")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.mlp.fc1.bias", F"encoder.encoder.layer.{i}.intermediate.dense.bias")				)
              rename_keys.append(
                  (F"encoder.deit.blocks.{i}.mlp.fc2.weight", F"encoder.encoder.layer.{i}.output.dense.weight")				)
              rename_keys.append((F"encoder.deit.blocks.{i}.mlp.fc2.bias", F"encoder.encoder.layer.{i}.output.dense.bias")				)

       # cls token, position embeddings and patch embeddings of encoder
       rename_keys.extend(
           [
               ("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
               ("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
               ("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
               ("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
               ("encoder.deit.norm.weight", "encoder.layernorm.weight"),
               ("encoder.deit.norm.bias", "encoder.layernorm.bias"),
           ]				)

       return rename_keys



def 	_lowerCamelCase(	a   ,    a				):
       for i in range(encoder_config.num_hidden_layers				):
              # queries, keys and values (only weights, no biases)
              __a       =    state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"				)

              __a       =    in_proj_weight[
                  : encoder_config.hidden_size, :
              ]
              __a       =    in_proj_weight[
                  encoder_config.hidden_size : encoder_config.hidden_size * 2, :
              ]
              __a       =    in_proj_weight[
                  -encoder_config.hidden_size :, :
              ]



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    dct.pop(a				)
       __a       =    val



def 	_lowerCamelCase(	a				):
       if "handwritten" in checkpoint_url:
              __a       =    "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg"  # industry
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
       elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
              __a       =    "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
       __a       =    Image.open(requests.get(a   ,    stream=a				).raw				).convert("RGB"				)
       return im



@torch.no_grad()
def 	_lowerCamelCase(	a   ,    a				):
       __a       =    ViTConfig(image_size=3_8_4   ,    qkv_bias=a				)
       __a       =    TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              __a       =    7_6_8
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              __a       =    1_0_2_4
              __a       =    4_0_9_6
              __a       =    2_4
              __a       =    1_6
              __a       =    1_0_2_4
       else:
              raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

       # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
       if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
              __a       =    False
              __a       =    "relu"
              __a       =    1_0_2_4
              __a       =    True
              __a       =    False
              __a       =    False

       # load HuggingFace model
       __a       =    ViTModel(a   ,    add_pooling_layer=a				)
       __a       =    TrOCRForCausalLM(a				)
       __a       =    VisionEncoderDecoderModel(encoder=a   ,    decoder=a				)
       model.eval()

       # load state_dict of original model, rename some keys
       __a       =    torch.hub.load_state_dict_from_url(a   ,    map_location="cpu"   ,    check_hash=a				)["model"]

       __a       =    create_rename_keys(a   ,    a				)
       for src, dest in rename_keys:
              rename_key(a   ,    a   ,    a				)
       read_in_q_k_v(a   ,    a				)

       # remove parameters we don't need
       del state_dict["encoder.deit.head.weight"]
       del state_dict["encoder.deit.head.bias"]
       del state_dict["decoder.version"]

       # add prefix to decoder keys
       for key, val in state_dict.copy().items():
              __a       =    state_dict.pop(a				)
              if key.startswith("decoder"				) and "output_projection" not in key:
                     __a       =    val
              else:
                     __a       =    val

    # load state dict
       model.load_state_dict(a				)

       # Check outputs on an image
       __a       =    ViTImageProcessor(size=encoder_config.image_size				)
       __a       =    RobertaTokenizer.from_pretrained("roberta-large"				)
       __a       =    TrOCRProcessor(a   ,    a				)

       __a       =    processor(images=prepare_img(a				)   ,    return_tensors="pt"				).pixel_values

       # verify logits
       __a       =    torch.tensor([[model.config.decoder.decoder_start_token_id]]				)
       __a       =    model(pixel_values=a   ,    decoder_input_ids=a				)
       __a       =    outputs.logits

       __a       =    torch.Size([1, 1, 5_0_2_6_5]				)
       if "trocr-base-handwritten" in checkpoint_url:
              __a       =    torch.tensor(
                  [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]				)
       elif "trocr-large-handwritten" in checkpoint_url:
              __a       =    torch.tensor(
                  [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]				)
       elif "trocr-base-printed" in checkpoint_url:
              __a       =    torch.tensor(
                  [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]				)
       elif "trocr-large-printed" in checkpoint_url:
              __a       =    torch.tensor(
                  [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]				)

       if "stage1" not in checkpoint_url:
              assert logits.shape == expected_shape, "Shape of logits not as expected"
              assert torch.allclose(logits[0, 0, :1_0]   ,    a   ,    atol=1E-3				), "First elements of logits not as expected"

       Path(a				).mkdir(exist_ok=a				)
       print(F"Saving model to {pytorch_dump_folder_path}"				)
       model.save_pretrained(a				)
       print(F"Saving processor to {pytorch_dump_folder_path}"				)
       processor.save_pretrained(a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:int									=			argparse.ArgumentParser()

   parser.add_argument(
       """--checkpoint_url""",
       default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
       type=str,
       help="""URL to the original PyTorch checkpoint (.pth file).""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

"""simple docstring"""



import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

SCREAMING_SNAKE_CASE__:Union[str, Any]									=			2






class  snake_case__      :


 def __init__( self    ,		*,  # begin keyword-only arguments
     lowerCamelCase="<s>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase=None    ,		):
        __a ,   __a ,   __a ,   __a       =    bos, unk, pad, eos
        __a       =    []
        __a       =    []
        __a       =    {}
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        __a       =    self.add_symbol(lowerCamelCase						)
        if extra_special_symbols:
               for s in extra_special_symbols:
                      self.add_symbol(lowerCamelCase						)
        __a       =    len(self.symbols						)


 def __eq__( self    ,		lowerCamelCase						):
        return self.indices == other.indices


 def __getitem__( self    ,		lowerCamelCase						):
        if idx < len(self.symbols						):
               return self.symbols[idx]
        return self.unk_word


 def __len__( self						):
        return len(self.symbols						)


 def __contains__( self    ,		lowerCamelCase						):
        return sym in self.indices


 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase						):
        __a       =    cls()
        d.add_from_file(lowerCamelCase						)
        return d


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=1    ,		lowerCamelCase=False						):
        if word in self.indices and not overwrite:
               __a       =    self.indices[word]
               __a       =    self.count[idx] + n
               return idx
        else:
               __a       =    len(self.symbols						)
               __a       =    idx
               self.symbols.append(lowerCamelCase						)
               self.count.append(lowerCamelCase						)
               return idx


 def 						a__   ( self    ,		lowerCamelCase						):
        return 0






 def 						a__   ( self    ,		lowerCamelCase						):
        if isinstance(lowerCamelCase    ,		lowerCamelCase						):
               try:
                      with open(lowerCamelCase    ,		"r"    ,		encoding="utf-8"						) as fd:
                             self.add_from_file(lowerCamelCase						)
               except FileNotFoundError as fnfe:
                      raise fnfe
               except UnicodeError:
                      raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(lowerCamelCase						)						)
               return

        __a       =    f.readlines()
        __a       =    self._load_meta(lowerCamelCase						)

        for line in lines[indices_start_line:]:
               try:
                      __a ,   __a       =    line.rstrip().rsplit(" "    ,		1						)
                      if field == "#fairseq:overwrite":
                             __a       =    True
                             __a ,   __a       =    line.rsplit(" "    ,		1						)
                      else:
                             __a       =    False
                      __a       =    int(lowerCamelCase						)
                      __a       =    line
                      if word in self and not overwrite:
                             raise RuntimeError(
                                 "Duplicate word found when loading Dictionary: '{}'. "
                                 "Duplicate words can overwrite earlier ones by adding the "
                                 "#fairseq:overwrite flag at the end of the corresponding row "
                                 "in the dictionary file. If using the Camembert model, please "
                                 "download an updated copy of the model file.".format(lowerCamelCase						)						)
                      self.add_symbol(lowerCamelCase    ,		n=lowerCamelCase    ,		overwrite=lowerCamelCase						)
               except ValueError:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'"						)



def 	_lowerCamelCase(	a				):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       __a       =    dict((re.sub(R"@@$"   ,    ""   ,    a				), v) if k.endswith("@@"				) else (re.sub(R"$"   ,    "</w>"   ,    a				), v) for k, v in d.items()				)
       __a       =    "<s> <pad> </s> <unk>".split()
       # restore the special tokens
       for k in keep_keys:
              del da[F"{k}</w>"]
              __a       =    d[k]  # restore
       return da



def 	_lowerCamelCase(	a   ,    a				):
       # prep
       if not os.path.exists(a				):
              raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"				)
       os.makedirs(a   ,    exist_ok=a				)
       print(F"Writing results to {pytorch_dump_folder_path}"				)

       # handle various types of models

       __a       =    os.path.join(a   ,    "checkpoint.pt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {checkpoint_file} does not exist!"				)
       __a       =    torch.load(a   ,    map_location="cpu"				)

       __a       =    chkpt["cfg"]["model"]

       # dicts
       __a       =    os.path.join(a   ,    "dict.txt"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {dict_file} does not exist!"				)
       __a       =    Dictionary.load(a				)
       __a       =    rewrite_dict_keys(src_dict.indices				)
       __a       =    len(a				)
       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["vocab_file"]				)
       print(F"Generating {src_vocab_file} of {src_vocab_size} records"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # merges_file (bpecodes)
       __a       =    os.path.join(a   ,    "bpecodes"				)
       if not os.path.isfile(a				):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"				)

       __a       =    os.path.join(a   ,    VOCAB_FILES_NAMES["merges_file"]				)
       shutil.copyfile(a   ,    a				)

       # model config
       __a       =    os.path.join(a   ,    "config.json"				)

       __a       =    {
           "activation_dropout": args["activation_dropout"],
           "architectures": ["BioGptForCausalLM"],
           "attention_probs_dropout_prob": args["attention_dropout"],
           "bos_token_id": 0,
           "eos_token_id": 2,
           "hidden_act": args["activation_fn"],
           "hidden_dropout_prob": args["dropout"],
           "hidden_size": args["decoder_embed_dim"],
           "initializer_range": 0.02,
           "intermediate_size": args["decoder_ffn_embed_dim"],
           "layer_norm_eps": 1E-1_2,
           "layerdrop": args["decoder_layerdrop"],
           "max_position_embeddings": args["max_target_positions"],
           "model_type": "biogpt",
           "num_attention_heads": args["decoder_attention_heads"],
           "num_hidden_layers": args["decoder_layers"],
           "pad_token_id": 1,
           "scale_embedding": not args["no_scale_embedding"],
           "tie_word_embeddings": args["share_decoder_input_output_embed"],
           "vocab_size": src_vocab_size,
       }

       # good hparam defaults to start with

       print(F"Generating {biogpt_model_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # tokenizer config
       __a       =    os.path.join(a   ,    a				)

       __a       =    {
           "bos_token": "<s>",
           "eos_token": "</s>",
           "model_max_length": 1_0_2_4,
           "pad_token": "<pad>",
           "special_tokens_map_file": None,
           "tokenizer_class": "BioGptTokenizer",
           "unk_token": "<unk>",
       }

       print(F"Generating {biogpt_tokenizer_config_file}"				)
       with open(a   ,    "w"   ,    encoding="utf-8"				) as f:
              f.write(json.dumps(a   ,    ensure_ascii=a   ,    indent=a				)				)

       # model
       __a       =    chkpt["model"]

       # remove unneeded keys
       __a       =    [
           "decoder.version",
       ]
       for k in ignore_keys:
              model_state_dict.pop(a   ,    a				)

       __a       =    list(model_state_dict.keys()				)
       for layer_name in layer_names:
              if layer_name.endswith("output_projection.weight"				):
                     __a       =    model_state_dict.pop(a				)
              else:
                     __a       =    model_state_dict.pop(a				)

       __a       =    BioGptConfig.from_pretrained(a				)
       __a       =    BioGptForCausalLM(a				)

       # check that it loads ok
       model_new.load_state_dict(a				)

       # save
       __a       =    os.path.join(a   ,    a				)
       print(F"Generating {pytorch_weights_dump_path}"				)
       torch.save(a   ,    a				)

       print("Conversion is done!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--biogpt_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help=(
           """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
           """ bpecodes, etc."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
